


import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


lowerCamelCase__							:						int							     =   getLogger(__name__)


lowerCamelCase__							:						Optional[int]							     =   """cuda""" if torch.cuda.is_available() else """cpu"""







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase = 8							,   __lowerCAmelCase = DEFAULT_DEVICE							,   __lowerCAmelCase=False							,   __lowerCAmelCase="summarization"							,   __lowerCAmelCase=None							,   **__lowerCAmelCase							,   )    ->				Dict:
  snake_case__						= Path(__lowerCAmelCase				).open('''w'''							,   encoding='''utf-8'''				)
  snake_case__						= str(__lowerCAmelCase				)
  snake_case__						= AutoModelForSeqaSeqLM.from_pretrained(__lowerCAmelCase				).to(__lowerCAmelCase				)
  if fpaa:
    snake_case__						= model.half()

  snake_case__						= AutoTokenizer.from_pretrained(__lowerCAmelCase				)
  logger.info(F"""Inferred tokenizer type: {tokenizer.__class__}"""				)  # if this is wrong, check config.model_type.

  snake_case__						= time.time()
  # update config with task specific params
  use_task_specific_params(__lowerCAmelCase							,   __lowerCAmelCase				)
  if prefix is None:
    snake_case__						= prefix or getattr(model.config							,   '''prefix'''							,   ''''''				) or ''''''
  for examples_chunk in tqdm(list(chunks(__lowerCAmelCase							,   __lowerCAmelCase				)				)				):
    snake_case__						= [prefix + text for text in examples_chunk]
    snake_case__						= tokenizer(__lowerCAmelCase							,   return_tensors='''pt'''							,   truncation=__lowerCAmelCase							,   padding='''longest'''				).to(__lowerCAmelCase				)
    snake_case__						= model.generate(
        input_ids=batch.input_ids							,   attention_mask=batch.attention_mask							,   **__lowerCAmelCase							,   )
    snake_case__						= tokenizer.batch_decode(__lowerCAmelCase							,   skip_special_tokens=__lowerCAmelCase							,   clean_up_tokenization_spaces=__lowerCAmelCase				)
    for hypothesis in dec:
      fout.write(hypothesis + '''\n'''				)
      fout.flush()
  fout.close()
  snake_case__						= int(time.time() - start_time				)  # seconds
  snake_case__						= len(__lowerCAmelCase				)
  return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs							,   4				)}







def SCREAMING_SNAKE_CASE	(			)    ->				Tuple:
  return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S'''				)







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase=True				)    ->				str:
  snake_case__						= argparse.ArgumentParser()
  parser.add_argument('''model_name'''							,   type=__lowerCAmelCase							,   help='''like facebook/bart-large-cnn,t5-base, etc.'''				)
  parser.add_argument('''input_path'''							,   type=__lowerCAmelCase							,   help='''like cnn_dm/test.source'''				)
  parser.add_argument('''save_path'''							,   type=__lowerCAmelCase							,   help='''where to save summaries'''				)
  parser.add_argument('''--reference_path'''							,   type=__lowerCAmelCase							,   required=__lowerCAmelCase							,   help='''like cnn_dm/test.target'''				)
  parser.add_argument('''--score_path'''							,   type=__lowerCAmelCase							,   required=__lowerCAmelCase							,   default='''metrics.json'''							,   help='''where to save metrics'''				)
  parser.add_argument('''--device'''							,   type=__lowerCAmelCase							,   required=__lowerCAmelCase							,   default=__lowerCAmelCase							,   help='''cuda, cuda:1, cpu etc.'''				)
  parser.add_argument(
      '''--prefix'''							,   type=__lowerCAmelCase							,   required=__lowerCAmelCase							,   default=__lowerCAmelCase							,   help='''will be added to the begininng of src examples'''				)
  parser.add_argument('''--task'''							,   type=__lowerCAmelCase							,   default='''summarization'''							,   help='''used for task_specific_params + metrics'''				)
  parser.add_argument('''--bs'''							,   type=__lowerCAmelCase							,   default=8							,   required=__lowerCAmelCase							,   help='''batch size'''				)
  parser.add_argument(
      '''--n_obs'''							,   type=__lowerCAmelCase							,   default=-1							,   required=__lowerCAmelCase							,   help='''How many observations. Defaults to all.'''				)
  parser.add_argument('''--fp16'''							,   action='''store_true'''				)
  parser.add_argument('''--dump-args'''							,   action='''store_true'''							,   help='''print the custom hparams with the results'''				)
  parser.add_argument(
      '''--info'''							,   nargs='''?'''							,   type=__lowerCAmelCase							,   const=datetime_now()							,   help=(
          '''use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'''
          ''' lang=en-ru. If no value is passed, the current datetime string will be used.'''
      )							,   )
  # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
  snake_case__				,				snake_case__						= parser.parse_known_args()
  snake_case__						= parse_numeric_n_bool_cl_kwargs(__lowerCAmelCase				)
  if parsed_args and verbose:
    print(F"""parsed the following generate kwargs: {parsed_args}"""				)
  snake_case__						= [''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path				).readlines()]
  if args.n_obs > 0:
    snake_case__						= examples[: args.n_obs]
  Path(args.save_path				).parent.mkdir(exist_ok=__lowerCAmelCase				)

  if args.reference_path is None and Path(args.score_path				).exists():
    warnings.warn(F"""score_path {args.score_path} will be overwritten unless you type ctrl-c."""				)

  if args.device == "cpu" and args.fpaa:
    # this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
    raise ValueError('''Can\'t mix --fp16 and --device cpu'''				)

  snake_case__						= generate_summaries_or_translations(
      __lowerCAmelCase							,   args.save_path							,   args.model_name							,   batch_size=args.bs							,   device=args.device							,   fpaa=args.fpaa							,   task=args.task							,   prefix=args.prefix							,   **__lowerCAmelCase							,   )

  if args.reference_path is None:
    return {}

  # Compute scores
  snake_case__						= calculate_bleu if '''translation''' in args.task else calculate_rouge
  snake_case__						= [x.rstrip() for x in open(args.save_path				).readlines()]
  snake_case__						= [x.rstrip() for x in open(args.reference_path				).readlines()][: len(__lowerCAmelCase				)]
  snake_case__						= score_fn(__lowerCAmelCase							,   __lowerCAmelCase				)
  scores.update(__lowerCAmelCase				)

  if args.dump_args:
    scores.update(__lowerCAmelCase				)
  if args.info:
    snake_case__						= args.info

  if verbose:
    print(__lowerCAmelCase				)

  if args.score_path is not None:
    json.dump(__lowerCAmelCase							,   open(args.score_path							,   '''w'''				)				)

  return scores


if __name__ == "__main__":
   # Usage for MT:
   # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
   run_generate(verbose=True)


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE 		=      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE 		=      {
    """sail/poolformer_s12""": """https://huggingface.co/sail/poolformer_s12/resolve/main/config.json""",
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
   __lowerCAmelCase           =				"""poolformer"""







   def __init__(     self						:						str   ,   lowerCamelCase_						:						Any=3   ,   lowerCamelCase_						:						Any=16   ,   lowerCamelCase_						:						Any=16   ,   lowerCamelCase_						:						Optional[Any]=3   ,   lowerCamelCase_						:						List[str]=4.0   ,   lowerCamelCase_						:						Tuple=[2, 2, 6, 2]   ,   lowerCamelCase_						:						Optional[Any]=[64, 128, 320, 512]   ,   lowerCamelCase_						:						Tuple=[7, 3, 3, 3]   ,   lowerCamelCase_						:						Optional[Any]=[4, 2, 2, 2]   ,   lowerCamelCase_						:						Dict=[2, 1, 1, 1]   ,   lowerCamelCase_						:						Tuple=4   ,   lowerCamelCase_						:						Optional[int]=0.0   ,   lowerCamelCase_						:						Union[str, Any]="gelu"   ,   lowerCamelCase_						:						Union[str, Any]=True   ,   lowerCamelCase_						:						Union[str, Any]=1E-5   ,   lowerCamelCase_						:						int=0.0_2   ,   **lowerCamelCase_						:						str   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      num_channels
    UpperCamelCase			  =      patch_size
    UpperCamelCase			  =      stride
    UpperCamelCase			  =      padding
    UpperCamelCase			  =      pool_size
    UpperCamelCase			  =      hidden_sizes
    UpperCamelCase			  =      mlp_ratio
    UpperCamelCase			  =      depths
    UpperCamelCase			  =      patch_sizes
    UpperCamelCase			  =      strides
    UpperCamelCase			  =      num_encoder_blocks
    UpperCamelCase			  =      drop_path_rate
    UpperCamelCase			  =      hidden_act
    UpperCamelCase			  =      use_layer_scale
    UpperCamelCase			  =      layer_scale_init_value
    UpperCamelCase			  =      initializer_range
    super().__init__(**lowerCamelCase_				)





class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
   __lowerCAmelCase           =				version.parse("""1.11"""		)







   @property
   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    return OrderedDict(
        [
            ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
        ]				)







   @property
   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    return 2E-3
